Characterization of the air flow in a scale model of a hydrogenerator by means of particle image velocimetry
Bibliographic record
Abstract
In hydroelectric power plants, generators are essential components and, like all machines, generate heat due to losses. The most common way to evacuate this heat is by circulating a cooling fluid (generally air) through the generator components. Due to their geometrical complexity, it is quite challenging to numerically simulate the flow to predict the cooling in a generator. Furthermore, in situ measurements are costly and difficult to perform due to the limited access. For this reason, a 1:4 scale model of a hydroelectric generator was built at the research institute of Hydro-Quebec (IREQ). In this thesis, particle image velocimetry (PIV) measurements of the flow in the opening of the generator pit, in the space between the enclosure wall and the cooler exit of the scale model, at the cooler exit, in the covers, and in theair gap and interpole region are presented. Experimental aspects pertaining to the seeding of the flow, calibration targets, experimental method and PIV theory are also discussed. Furthermore, a comparison of the experimental data with the results of CFD (Computational Fluid Dynamics) simulations using ANSYS-CFX is given. The results in the pit opening region have shown the sensibility of the simulation results to small modifications to the geometry. The measurements in the space between the cooler exit and the enclosure wall and those in the air gap and interpole region have qualitatively validated the CFD. Finally, computation of the mass flow rate through the cooler exit and in the cover has also quantitatively validated the simulation results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".